The present invention relates to the architecture of computer systems.
The architecture of many of the present generation of commercially available computers is termed "Von Neumann", based upon the work of John Von Neumann in the 1940's. In Von Neumann computer architecture, the computer memory is physically separated from the computer processor and the two are linked by communication link. The speed of that communication link limits the speed of the entire computer system, i.e., it is a "bottle-neck".
Such conventional Von Neumann computers often have a single arithmetic unit; one memory for both instructions and data; and one address bus for both instructions and data. The data bus and address bus are the communication link. Signal processing computers frequently use an alternative computer architecture (the so-called "Harvard architecture"). The Harvard architecture uses one arithmetic unit, with separate hardware for address calculations; two memories, one for instructions and one for data; two data buses, one for instructions and one for data; and two address buses, one for instructions, and one for data. In addition, parallel combinations of either Von Neumann or Harvard computer systems or special purpose units have been proposed by various researchers. Most commonly, such systems are locally connected, since complete connectivity over many buses would be impractical
In a conventional "array processor", in which a number of individual computers are connected to a central memory, long vectors are required in order to approach peak throughput.
A number of different approaches to computer architecture have been suggested in recent years based on analogies to the complex interrelationship of nerve cells in the brain. These computer systems are called "neural-net computers" (NN) or "artificial neural systems (ANS)" or "neurocomputers".
In general, the neural-net computers are connected in feedback patterns, which distinguish them from the earlier "perceptron networks", which emphasized feed-forward connectivity.
The field of neural-net computers may be considered part of the broader field of "artificial intelligence" (AI) which utilizes analogies based upon human thought processes. However, AI generally utilizes conventional digital computers having Von Neumann architecture with its communications link bottleneck. AI is generally directed to the computer programs ("software") for conventional digital computers and seeks to program functions associated with human thought, such as reasoning, learning and self-improvement.
Although the general concept and goal of designing neural-net computers has been envisioned for a few years, there is no agreement as to how the nerve cells of the human are connected or the architecture of a computer which would mimic or be an analogy to such nerve cell connections.
The literature on the subject indicates that it is a hope that neural net computers will be better able to recognize "patterns", i.e., to select and distinguish a form, such as a wave form, from a mass of similar forms, although the selected form is not exactly the same as the ideal pattern. Such pattern recognition and machine vision systems are particularly significant in instances where the patterns are "fuzzy", i.e., few or none are exact replicas of the original ideal pattern. Pattern recognition is of prime importance in speech recognition systems, handwriting and print recognition systems, object recognition for robotics, radar systems and medical diagnosis systems, such as EEG (electroencephalograph) systems.